By testing 92 different biochars,this study had the objective to determine the relations between simple physico-chemical characteristics of biochar(elemental composition,ash fraction,specific surface area,process para...By testing 92 different biochars,this study had the objective to determine the relations between simple physico-chemical characteristics of biochar(elemental composition,ash fraction,specific surface area,process parameters)and infrared sorption characteristics revealing the presence of specific functional groups.The results of Diffusive Reflection Fourier Transformation Infrared Spectroscopy were statistically analyzed with multiple linear regression techniques and Principal Component Analysis(PCA)with the biochar characterization parameters as model inputs.The dominant parameters affecting the functional group signals were the pyrolysis temperature,the H/C-ratio and specific surface area of the biochar and the ash fraction.Regression models were able to explain 60-90%of data variance for specific peaks in the DRIFTS-spectra.The application of PCA could further reduce the input parameters to three main factors explaining 70.8%of total data variance.Biplots of the first two main factors showed the similarity of infrared spectra of biochars produced at 300 and 450℃,whereas a pyrolysis temperature of 600℃ lead to a partial and a 750℃ to a nearly complete loss of biochar surface functional groups.展开更多
基金financial support by the Austrian FWF(Austrian Science Fund),Project Nr.I 3133-N34by the Czech Science Foundation(GF17-33820L)。
文摘By testing 92 different biochars,this study had the objective to determine the relations between simple physico-chemical characteristics of biochar(elemental composition,ash fraction,specific surface area,process parameters)and infrared sorption characteristics revealing the presence of specific functional groups.The results of Diffusive Reflection Fourier Transformation Infrared Spectroscopy were statistically analyzed with multiple linear regression techniques and Principal Component Analysis(PCA)with the biochar characterization parameters as model inputs.The dominant parameters affecting the functional group signals were the pyrolysis temperature,the H/C-ratio and specific surface area of the biochar and the ash fraction.Regression models were able to explain 60-90%of data variance for specific peaks in the DRIFTS-spectra.The application of PCA could further reduce the input parameters to three main factors explaining 70.8%of total data variance.Biplots of the first two main factors showed the similarity of infrared spectra of biochars produced at 300 and 450℃,whereas a pyrolysis temperature of 600℃ lead to a partial and a 750℃ to a nearly complete loss of biochar surface functional groups.
